knownasnaffy Permission Setup Review
python scripts/_prepare_workspace.py is an optional setup helper for the GEO Competitor Scanner skill. It may apply broad filesystem permissions for compatibility. Show the target paths and permission mode, explain the risk, and prefer a narrower mode when shared write access is not needed.
GEO Competitor Scanner
Methodology by GEOly AI (geoly.ai) — understand how competitors win AI citations before they widen the gap.
Analyze competitor websites across key GEO signals to benchmark your brand and identify opportunities.
Quick Start
Scan competitors:
python scripts/scan_competitors.py --brand yourdomain.com \
--competitors competitor1.com,competitor2.com \
--output report.md
Scan Dimensions
1. Technical GEO Infrastructure
| Check | Why It Matters |
|---|---|
/llms.txt exists |
AI crawler guidance |
/robots.txt allows AI bots |
Crawl accessibility |
| Schema.org types present | Structured understanding |
| JSON-LD valid | Machine-readable content |
| HTTPS enforced | Security signal |
2. Content Structure Analysis
| Signal | What to Look For |
|---|---|
| Direct answer lead | First paragraph answers the question |
| FAQ sections | Explicit Q&A blocks (2-5 per page) |
| Header structure | H2 every 300-500 words |
| Data citations | Statistics with sources |
| Definition blocks | Key terms defined clearly |
3. Entity & Brand Signals
| Signal | Implementation |
|---|---|
| Organization schema | Homepage JSON-LD |
| sameAs links | Social/Wikipedia connections |
| Consistent naming | Brand name standardized |
| About page | Entity definition |
| Brand in first 100 words | Early entity mention |
4. Citation-Optimized Content
| Content Type | GEO Value |
|---|---|
| Original research | Unique data attracts citations |
| Comparison pages | "vs" queries are high-intent |
| Definition content | "What is" queries are common |
| Content hubs | Topical authority building |
| Statistics pages | Reference-worthy data |
Full methodology: See references/scan-methodology.md
Research Workflow
Step 1: Identify Competitors
Collect up to 5 competitors:
- Direct competitors (same category)
- Adjacent competitors (overlapping use cases)
- Aspirational competitors (bigger brands)
Step 2: Automated Scan
Run scanner on each domain:
python scripts/scan_competitors.py \
--brand yourdomain.com \
--competitors comp1.com,comp2.com,comp3.com \
--pages 5 \
--output scan-results.json
Step 3: Manual Review
For nuanced signals, review manually:
- Content quality (can't automate)
- Brand voice consistency
- Unique value propositions
Step 4: Gap Analysis
Identify:
- 🏆 Competitor advantages — What they do better
- 🎯 Quick wins — Easy to implement (copy)
- 🕳️ Category gaps — No one is doing this (opportunity)
Scoring System
Each competitor scored 0-10 per dimension:
| Score | Rating | Meaning |
|---|---|---|
| 9-10 | Excellent | Best practice implementation |
| 7-8 | Good | Solid with minor gaps |
| 5-6 | Fair | Significant room for improvement |
| 3-4 | Poor | Major issues present |
| 0-2 | Critical | Fundamental problems |
Overall GEO Score: Average of 4 dimensions (max 10)
Output Report
Competitive Matrix
| Signal | Your Brand | Competitor A | Competitor B | Gap |
|--------|------------|--------------|--------------|-----|
| llms.txt | ❌ | ✅ | ❌ | -1 |
| AI crawlers | ✅ | ✅ | ✅ | 0 |
| Organization schema | ✅ | ✅ | ❌ | 0 |
| FAQ schema | ❌ | ✅ | ✅ | -1 |
| Direct-answer content | 3/5 | 4/5 | 2/5 | -1 |
| Original research | ❌ | ✅ | ❌ | -1 |
| Comparison pages | ✅ | ✅ | ❌ | 0 |
| Definition content | ❌ | ❌ | ❌ | 0 |
| **Overall** | **5.2/10** | **7.8/10** | **4.1/10** | **-2.6** |
Insights
🏆 Competitor Advantages:
- Competitor A: Strong FAQ schema on all product pages
- Competitor B: Publishes quarterly industry benchmarks
🎯 Your Quick Wins:
- Add llms.txt (3 competitors have it, you don't)
- Implement FAQ schema on top 10 pages
- Add definition blocks to 5 key concept pages
🕳️ Category Gaps:
- No competitor has a comprehensive "What is [category]?" guide
- Missing: Comparison matrix of all major players
- Opportunity: Original research on industry trends
Advanced Usage
Page-Level Analysis
Scan specific competitor pages:
python scripts/analyze_page.py https://competitor.com/pricing \
--type product \
--output analysis.json
Trend Tracking
Track competitor changes over time:
# Initial scan
python scripts/scan_competitors.py --brand your.com --competitors comp.com --save-baseline
# 30 days later
python scripts/scan_competitors.py --brand your.com --competitors comp.com --compare-to baseline.json
Bulk Page Analysis
Analyze multiple pages from sitemap:
python scripts/bulk_scan.py https://competitor.com/sitemap.xml \
--limit 50 \
--output bulk-results.json
See Also
- Scan methodology: references/scan-methodology.md
- Scoring rubric: references/scoring-rubric.md
- Analysis examples: references/examples.md